When expected counts in a chi square test are very low, what can this imply?

When expected counts in a chi-square test are very low, this can imply potential issues with the validity of the results. In a chi-square test, one of the assumptions is that the expected frequency for each category should be sufficiently large, generally considered to be at least 5. If the expected counts are low, it may lead to unreliable results, as the chi-square approximation may not hold true. This can increase the risk of Type I and Type II errors, potentially distorting the conclusions drawn from the test. It highlights the importance of appropriate sample sizes and distribution of data across categories, indicating that caution should be taken when interpreting the results under these conditions.